Soft matter physics principles can be used to address important problems in the food industry. Starch granules are widely used in foods to create desirable textural attributes, but high levels of digestible starch may pose a risk of diabetes. Consequently, there is a need to find healthier replacements for starch granules. The objective of this research was to create hydrogel particles from protein and dietary fiber with similar dimensions and functional attributes as starch granules. Hydrogel particles were formed by mixing gelatin (0.5 wt%) with pectin (0 to 0.2 wt%) at pH values above the isoelectric point of the gelatin (pH 9, 30 °C). When the pH was adjusted to pH 5, the biopolymer mixture spontaneously formed micron-sized particles due to electrostatic attraction of cationic gelatin with anionic pectin through complex coacervation. Differential interference contrast (DIC) microscopy showed that the hydrogel particles were translucent and spheroid, and that their dimensions were determined by pectin concentration. At 0.01 wt% pectin, hydrogel particles with similar dimensions to swollen starch granules (D3,2 ≈ 23 µm) were formed. The resulting hydrogel suspensions had similar appearances to starch pastes and could be made to have similar textural attributes (yield stress and shear viscosity) by adjusting the effective hydrogel particle concentration. These hydrogel particles may therefore be used to improve the texture of reduced-calorie foods and thereby help tackle obesity and diabetes.

Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on. Many algorithms try to predict model performance of the pruned sub-nets by introducing various evaluation methods. But they are either inaccurate or very complicated for general application. In this work, we present a pruning method called EagleEye, in which a simple yet efficient evaluation component based on adaptive batch normalization is applied to unveil a strong correlation between different pruned DNN structures and their final settled accuracy. This strong correlation allows us to fast spot the pruned candidates with highest potential accuracy without actually fine-tuning them. This module is also general to plug-in and improve some existing pruning algorithms. EagleEye achieves better pruning performance than all of the studied pruning algorithms in our experiments. Concretely, to prune MobileNet V1 and ResNet-50, EagleEye outperforms all compared methods by up to 3.8%. Even in the more challenging experiments of pruning the compact model of Mo-bileNet V1, EagleEye achieves the highest accuracy of 70.9% with an overall 50% operations (FLOPs) pruned. All accuracy results are Top-1 ImageNet classification accuracy. Source code and models are accessible to open-source community. 3

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Robotic ultrasound systems have turned into clinical use over the past few decades, increasing precision and quality of medical operations. In this paper, we propose a fully automatic scanning system for three-dimensional (3-D) ultrasound imaging. A depth camera was first used to obtain the depth data and color data of the tissue surface. Based on the depth image, the 3-D contour of the tissue was rendered and the scan path of ultrasound probe was automatically planned. Following the scan path, a 3-D translating device drove the probe to move on the tissue surface. Simultaneously, the B-scans and their positional information were recorded for subsequent volume reconstruction. In order to stop the scanning process when the pressure on the skin exceeded a preset threshold, two force sensors were attached to the front side of the probe for force measurement. In vitro and in vivo experiments were conducted for assessing the performance of the proposed system. Quantitative results show that the error of volume measurement was less than 1%, indicating that the system is capable of automatic ultrasound scanning and 3-D imaging. It is expected that the proposed system can be well used in clinical practices.

Self-assembled Ni­(OH)2 nanosheet-decorated hierarchical flower-like MnCo2O4.5 nanoneedles were synthesized via a cost-effective and facile hydrothermal strategy, aiming to realize a high-capacity advanced electrode of a battery–supercapacitor hybrid (BSH) device. It is demonstrated that the as-synthesized hierarchical flower-like MnCo2O4.5@Ni­(OH)2-nanosheet electrode exhibits a high specific capacity of 318 mAh g–1 at a current density of 3 A g–1 and still maintains a capacity of 263.5 mAh g–1 at a higher current density of 20 A g–1, with an extremely long cycle lifespan of 87.7% capacity retention after 5000 cycles. Moreover, using the unique core–shell structure as the cathode and hollow Fe2O3 nanoparticles/reduced graphene oxide as the anode, the BSH device delivers a high energy density of 56.53 Wh kg–1 when the power density reaches 1.9 kW kg–1, and there is an extraordinarily good cycling stability with the capacity retention rate of 90.4% after 3000 cycles. It is believed that the superior properties originate from desirable core–shell structures alleviating the impact of volume changes as well as the existence of two-dimensional Ni­(OH)2 nanosheets with more active sites, thereby improving the cycle stability and achieving ultrahigh capacity. These results will provide more access to the rational material design of diverse nanostructures toward high-performance energy storage devices.